Impact of Convergence of Smart-Technology as Compared to Traditional Methodological Tools on Fostering Cognitive Aspects of Leadership Competencies in the Process of Vocational Training of Students
Bibliographic record
Abstract
The main objective of this research is to explore how effective and efficient the convergent use of traditional and smart technology tools could be when deployed in fostering leadership competencies of the students in the settings of tertiary vocational education. The experiment involved the students of two universities doing the elective course “Do Better Your Leadership Skills Up”. Having been split up into two halves, the first part of the focus group used the traditional forms of educational process, while the second one additionally used the software like CogniFit, Lumosity, BrainHQ, NeuroNation, Brain Metrix, Eidetic, Fit Brains, BrainExer 2.0. At the entry stage, the pedagogic surveys had been used as well as the cognitive function test to study the cognitive capabilities of the focus group students. We used a multi method approach of combining the close-ended and open-ended questions to get the feedback and the above cognitive test to measure the output of the study. Quantitative methods had been used to analyze the data and such Covariance-based Structural Equation Modeling (SEM) software as SPSS AMOS had been applied to evaluate the results because cognitive function of a person includes sub-components of latent constructs. Textalyzer software had been used to process the students’ responses to open-ended questions of the questionnaire for the most commonly used positive words in the texts, which helped us to identify broad categories of responses. Here, the most commonly used words we had distinguished were “involvement”, “improvement”, “gamification”, “motivation”, “speed”, “concentration”, “memory”, “current studies”, “future job”. Then we distributed the answers by the frequency of the identified words. The responses, which fell under no category, had been analyzed manually. The experimentally obtained data shows that integration of the smart technology into traditional learning environment increases students’ involvement by 23%, personal transformation by 18% and motivation by 17%. Our study proves that the convergent mode of instruction brings more benefits to the students in terms of fostering cognitive aspects of leadership competencies in the process of vocational training than the traditional mode. We found that the converged pedagogical mode enhances the collaboration and involvement of all the stakeholders of educational process. It makes students achieve the greatest personal satisfaction through enhanced self-esteem, efficiency gains, a sense of continuous personal achievement and enhanced autonomy and experimenting with their own learning strategies. We suggest universities (of Ukraine, specifically) to provide training to the teachers with all the latest technology, which seems essential for teaching. Academic institutions (of Ukraine) should also invest into research in the area of the educational-purpose use of smart technology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".